Multi-Environment POMDPs with Finite-Horizon Objectives
📰 ArXiv cs.AI
Learn to compute optimal value and policy in Multi-Environment POMDPs with finite-horizon objectives, a PSPACE-complete problem, and apply it to real-world decision-making under uncertainty
Action Steps
- Formulate a MEPOMDP model for a given problem using partial observability and stochastic environments
- Compute the optimal value function using dynamic programming or other suitable methods
- Derive the optimal policy from the computed value function
- Apply the policy to a finite-horizon objective in a multi-environment setting
- Evaluate the performance of the policy using metrics such as expected reward or success rate
Who Needs to Know This
Researchers and engineers working on decision-making under uncertainty, particularly those in AI and robotics, can benefit from this knowledge to improve their systems' performance in complex environments
Key Insight
💡 MEPOMDPs with finite-horizon objectives are PSPACE-complete, requiring efficient algorithms for computation
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Key Takeaways
Learn to compute optimal value and policy in Multi-Environment POMDPs with finite-horizon objectives, a PSPACE-complete problem, and apply it to real-world decision-making under uncertainty
Full Article
Title: Multi-Environment POMDPs with Finite-Horizon Objectives
Abstract:
arXiv:2605.07537v1 Announce Type: new Abstract: Partially Observable Markov Decision Processes (POMDPs) are systems in which one agent interacts with a stochastic environment, and receives only partial information about the current state. In a multi-environment POMDP (MEPOMDP), the initial state is unknown, and assumed to be adversarially chosen. In this work we focus on computing the optimal value and policy in MEPOMDPs with finite-horizon objectives. That problem is known to be PSPACE-complete
Abstract:
arXiv:2605.07537v1 Announce Type: new Abstract: Partially Observable Markov Decision Processes (POMDPs) are systems in which one agent interacts with a stochastic environment, and receives only partial information about the current state. In a multi-environment POMDP (MEPOMDP), the initial state is unknown, and assumed to be adversarially chosen. In this work we focus on computing the optimal value and policy in MEPOMDPs with finite-horizon objectives. That problem is known to be PSPACE-complete
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